1. Field of the Invention
The present invention relates to an information signal processing device and processing method, codebook generating device and generating method, and program for executing the methods, suitable for applying to conversion of standard or low-definition-equivalent standard television signals (SD signals) into high-definition signals (HD signals).
In further detail, the present invention relates to an information signal processing device wherein, at the time of converting first information signals made up of a plurality of pieces of information data into second information signals made up of a plurality of pieces of information data, one or a plurality of pieces of information data making up the second information signals are generated for each block comprising one or a plurality of pieces of information data obtained by dividing the first information signals, based on data based on class taps of the information data contained in the blocks and on auxiliary data, whereby the first information signals can be suitably converted into second information signals.
More specifically, the present invention relates to an information signal processing device wherein, at the time of converting first information signals made up of a plurality of pieces of information data into second information signals made up of a plurality of pieces of information data, one or a plurality of pieces of information data making up the second information signals are generated for each block comprising one or a plurality of pieces of information data obtained by dividing the first information signals, based on the class code of class taps made up of information data contained in the blocks and features thereof, whereby the first information signals can be suitably converted into second information signals.
More specifically yet, the present invention relates to an information signal processing device wherein, at the time of converting first information signals made up of a plurality of pieces of information data into second information signals made up of a plurality of pieces of information data, one or a plurality of pieces of information data making up the second information signals are generated for each block comprising one or a plurality of pieces of information data obtained by dividing the first information signals, based on the class code of class taps made up of information data contained in the blocks, and further correcting this generated data using correction data obtained based on range information of the components making up error vectors which are obtained by performing subtraction between tap vectors having as the components thereof the information data contained in the class taps, and representative vectors corresponding to the class code, whereby the first information signals can be suitably converted into second information signals.
2. Description of the Related Art
There is a technique in image signal processing, for example, wherein image signals are classified into multiple classes, and processing is performed corresponding to each class, as described in Japanese Unexamined Patent Application Publication No. 7-95591 and Japanese Unexamined Patent Application Publication No. 2000-59740. For example, in the event that classification is performed based on the activity of image signals, image signals with high activity which have a great amount of change, and image signals with low activity which are flat can be classified separately, and accordingly can be subjected to processing suitable for each class.
According to such image signal processing with class classification as described above, image signal processing suitable for image signals of each class can be performed for each of the image signals divided into the classes, and accordingly the greater the number of classes, the more suitable processing should be able to be performed on the classified image signals, at least theoretically. However, in the event that the number of classes becomes great, the number of patterns of processing performed according to each class also becomes great, resulting in a massive device.
For example, in the event of classifying classes based on activity as in the example above, as many classes need to be prepared as the values which the activities are capable of assuming, so that processing suitable for, the activity of image signals of each activity can be carried out. However, in the event that N difference values of adjacent pixels of which a plurality are arrayed in the horizontal direction are employed as the activity for example, and in the event that the difference value is K bits, the total number of classes is a staggering (2K)N classes.
Accordingly, class classification is performed using some sort of compression processing, such as ADRC (Adaptive Dynamic Range Coding) or the like. With class classification using ADRC, the N difference values such as in the example above serving as the data used for the class classification (hereafter referred to as “class taps”) are subjected to ADRC processing, and the ADRC code obtained thereby is used for class determination.
With K-bit ADRC, for example, the maximal value MAX and minimal value MIN of the values of the data making up the class tap is detected, DR=MAX−MIN is set as a local dynamic range of the set, and based on this dynamic range DR, the minimal value MIN is subtracted from the values of the data making up the class tap, and the subtracted value is divided by DR/2K (quantized). A bit string, wherein the K-bit values regarding the data making up the class tap obtained as described above are arrayed in a predetermined order, is output as ADRC code.
Accordingly, in the event that the class tap is subjected to 1-bit ADRC processing for example, the minimal value MIN is subtracted from each piece of data making up the class tap, and then divided by (MAX−MIN)/2, so that each piece of data is 1 bit. The bit string wherein this 1-bit data is arrayed in a predetermined order is output as ADRC code.
Now, class classification can be performed by vector quantization for example, and the like, besides ADRC processing. In the event of performing class classification using compressing processing as described above, the number of classes can be reduced, but on the other hand, fine classification cannot be performed as with cases of performing class classification without compressing processing, meaning that suitable processing cannot be applied to the image signals.
For example, let us consider a case of image signal processing wherein class classification of first image signals is performed using vector quantization, so as to generate second image signals for each class. In this case, the first image signal is divided into blocks made up of multiple pieces of pixel data. Vectors having as the components thereof the multiple pieces of pixel data making up the blocks (hereafter also referred to as “block vectors”) are configured for each block. Further, each block vector is subjected to vector quantization using a pre-obtained codebook, and code (symbols) obtained as the result of the vector quantization is output as class code indicating the class of the block vector.
Pixel data for the second image signals is then generated for each class code. That is to say, the class code is subjected to inverse vector quantization using the codebook, and the block vector corresponding to the class code is obtained. A block containing the components of the block vector as pixel data is obtained, and the second image signals are generated by positioning the block at the corresponding position.
However, with the second image signals obtained by the inverse vector quantization as high-image quality image signals and the first image signals to be subjected to vector quantization as low-image quality image signals, the block vectors of the low-quality image signals which are to be subjected to vector quantization of the same class code are all subjected to inverse vector quantization by the same code vector, i.e., the block vector of the same high-quality image signals. This means that block vectors obtained by inverse block vector quantization have so-called quantization error. Accordingly, even though the image signals generated with the above-described processing are called high-quality image signals, the quality thereof has deteriorated by an amount corresponding to the quantization error in comparison with the high-quality image signals used for compiling the inverse quantization codebook.